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MODEL_EVALUATION.R
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MODEL_EVALUATION.R
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library(here)
source(here::here('libraries.R'))
#############################TRAINING PARAMETERS
###########WRF---DN
modelos_WRF_DN<- here::here('Data/Parques/Belesar/Modelos/WRF_DN/') %>% list.files(full.names = T) %>%
.[str_detect(., ".RDS")]
Lista_results<- list()
for (i in 1:length(modelos_WRF_DN)) {
Lista_results[[i]]<- tryCatch({readRDS(modelos_WRF_DN[i])}, error=function(e){0})
}
Lista_modelos_buenos<- Lista_results[sapply(Lista_results, function(x) length(class(x))==2)]
names(Lista_modelos_buenos)<- Lista_modelos_buenos %>% sapply(., function(x) x$method)
Tabla_results<- lapply(Lista_modelos_buenos, function(x) x$results[,((length(x$results)-5):length(x$results))]) %>%
bind_rows(.id ="Tipo_modelo")
Tabla_results %>% group_by(Tipo_modelo) %>% summarise(MRMSE=min(RMSE),MMAE=min(MAE)) %>%
as.data.frame() %>%
ggplot()+
geom_bar(aes(y=MRMSE, x=Tipo_modelo),
stat = "identity",
fill="red",
alpha=0.5)+
geom_bar(aes(y=MMAE, x=Tipo_modelo),
stat = "identity",
fill="blue",
alpha=0.5)+
ylab("RMSE---MAE")+
labs(subtitle = "Modelo WRF a diferencia nivel")+
theme_light()
###############################DN-----AP
modelos_WRF_DN<- here::here('Data/Parques/Belesar/Modelos/DN_AP/') %>% list.files(full.names = T) %>%
.[str_detect(., ".RDS")]
Lista_results<- list()
for (i in 1:length(modelos_WRF_DN)) {
Lista_results[[i]]<- tryCatch({readRDS(modelos_WRF_DN[i])}, error=function(e){0})
}
Lista_modelos_buenos<- Lista_results[sapply(Lista_results, function(x) length(class(x))==2)]
names(Lista_modelos_buenos)<- Lista_modelos_buenos %>% sapply(., function(x) x$method)
Tabla_results<- lapply(Lista_modelos_buenos, function(x) x$results[,((length(x$results)-5):length(x$results))]) %>%
bind_rows(.id ="Tipo_modelo")
Tabla_results %>% group_by(Tipo_modelo) %>% summarise(MRMSE=min(RMSE),MMAE=min(MAE)) %>%
as.data.frame() %>%
ggplot()+
geom_bar(aes(y=MRMSE, x=Tipo_modelo),
stat = "identity",
fill="red",
alpha=0.5)+
geom_bar(aes(y=MMAE, x=Tipo_modelo),
stat = "identity",
fill="blue",
alpha=0.5)+
ylab("RMSE---MAE")+
labs(subtitle = "Modelo WRF a diferencia nivel")+
theme_light()
Tabla_results %>% group_by(Tipo_modelo) %>% summarise(MRMSE=min(RMSE),MMAE=min(MAE)) %>% as.data.frame() %>% .[which.min(.[,3]),]
# #############################TEST PARAMETERS -----------------------------------------------------------
rmse <- function(error){sqrt(mean(error^2))}
# Function that returns Mean Absolute Error
mae <- function(error){mean(abs(error))}
if(!(exists("Tabla_1")&exists("Tabla_2")&exists("Tabla_3"))){
Obs_Data<- list.files(here::here('Data/Parques/Belesar/Historico/WEB/PM/'), full.names = T) %>%
.[str_detect(.,"Obs_")] %>% .[which.max( str_split(., "/") %>% lapply(., function(x) x[length(x)])%>% str_remove("Obs_|.RDS") %>%
str_replace("--"," ") %>% ymd_hms())] %>% readRDS()
WRF_data<- list.files(here::here('Data/Parques/Belesar/Historico/WEB/PM/'), full.names = T) %>%
.[str_detect(.,"WRF_")] %>% .[which.max( str_split(., "/") %>% lapply(., function(x) x[length(x)])%>% str_remove("WRF_|.RDS") %>%
str_replace("--"," ") %>% ymd_hms())] %>% readRDS()
#CREAMOS TABLA 1... LA TABLA COMPLETA QUE INCLUYE LOS DATOS DE LA PÁGINA WEB E HISTORICO DHI
#range(Tabla_1$Date)
# "2018-01-02 00:00:00 UTC" "2019-05-13 07:00:00 UTC"[ACTUALIDAD]
Tabla_1<- Obs_Data %>% mutate(diff_nivel=c(NA,diff(nivel)))
Tabla_1<- left_join(Tabla_1, WRF_data[[23]]$D1[,c("Date", "prep_hourly")], by="Date")
# CREEAMOS TABLA_2: ESTO SOLAMENTE COMPRENDE EL HISTÓRICO DE DHI. YA QUE ES LA FUENTE DE INFORMACION
# SOBRE APORTACION
#range(Tabla_2$Date)
#"2018-01-02 12:00:00 UTC" "2019-02-07 23:00:00 UTC"
Tabla_2<- Tabla_1
Tabla_2[,c("Vol","Temp", "porcentaje","prep_hourly")]<- NULL
Tabla_2<- Tabla_2[complete.cases(Tabla_2),]
Tabla_2$aport_SMA<- SMA(Tabla_2$aport, SMA_APORTACION)
Tabla_2$difnivel_SMA<- SMA(Tabla_2$diff_nivel, SMA_DIFF_NIVEL)
Tabla_2<- Tabla_2[complete.cases(Tabla_2),]
#CREAMOS TABLA_3: ESTO SOLAMENTE COMPRENDE DESDE FINALES DE OCTUBRE HASTA LA ACTUALIDAD
#range(Tabla_3$Date)
#"2018-10-25 01:00:00 UTC"----"2019-05-13 07:00:00 UTC"[ACTUALIDAD]
Tabla_3<- Tabla_1
Tabla_3[,c("Vol","Temp", "porcentaje", "aport")]<- NULL
Tabla_3<- Tabla_3[complete.cases(Tabla_3),]
#Tabla_3$aport_SMA<- SMA(Tabla_3$aport, SMA_APORTACION)
Tabla_3$difnivel_SMA<- SMA(Tabla_3$diff_nivel, SMA_DIFF_NIVEL)
Tabla_3$lluvia_SMA<- SMA(Tabla_3$lluvia, SMA_LLUVIA_OBS)
Tabla_3$WRF_SMA<- SMA(Tabla_3$prep_hourly, SMA_LLUVIA_WRF)
Tabla_3$WRF_SMA_lag<- lag(Tabla_3$WRF_SMA, LAG_LLUVIA_WRF)
Tabla_3$lluvia_SMA_lag<- lag(Tabla_3$lluvia_SMA, LAG_LLUVIA_OBS)
Tabla_3$DN_SMA_lag<- lag(Tabla_3$difnivel_SMA, LAG_DIFF_NIVEL)
Tabla_3<- Tabla_3[complete.cases(Tabla_3),]
#Por encima de octubre
Tabla_3<- Tabla_3[Tabla_3$Date>ymd("2018/10/25"), ]
}
################### WRF---DN
PORCENTAJE_ENTRENAMIENTO<- 0.8
LOGIC_TRAIN<- ifelse(1:nrow(Tabla_3)%in%(1:round(nrow(Tabla_3)*PORCENTAJE_ENTRENAMIENTO)), TRUE,FALSE)
prediccion_data<- Tabla_3[!LOGIC_TRAIN, ]
OBSERVED_AP<- prediccion_data_DN_A$aport_SMA
OBSERVED_DIFNIVEL<- prediccion_data$difnivel_SMA
modelos_WRF_DN<- here::here('Data/Parques/Belesar/Modelos/WRF_DN/') %>% list.files(full.names = T) %>%
.[str_detect(., ".RDS")]
Lista_results<- list()
for (i in 1:length(modelos_WRF_DN)) {
Lista_results[[i]]<- tryCatch({readRDS(modelos_WRF_DN[i])}, error=function(e){0})
}
Lista_modelos_buenos<- Lista_results[sapply(Lista_results, function(x) length(class(x))==2)]
names(Lista_modelos_buenos)<- Lista_modelos_buenos %>% sapply(., function(x) x$method)
Lista_parameters<-list()
for (i in 1:length(Lista_modelos_buenos)) {
MODEL_WRF_DN_PREDICTION<- predict(Lista_modelos_buenos[[i]], newdata= prediccion_data)
error<- OBSERVED_DIFNIVEL- MODEL_WRF_DN_PREDICTION
Lista_parameters[[i]] <- c(rmse(error), mae(error))
}
Tabla_results<- Lista_parameters %>% unlist() %>% matrix(ncol=2, byrow = T) %>% as.data.frame() %>%
cbind(names(Lista_modelos_buenos), . )
colnames(Tabla_results)<- c("Tipo_modelo", "RMSE", "MAE")
Tabla_results %>% group_by(Tipo_modelo) %>% summarise(MRMSE=min(RMSE),MMAE=min(MAE)) %>%
as.data.frame() %>%
ggplot()+
geom_bar(aes(y=MRMSE, x=Tipo_modelo),
stat = "identity",
fill="red",
alpha=0.5)+
geom_bar(aes(y=MMAE, x=Tipo_modelo),
stat = "identity",
fill="blue",
alpha=0.5)+
ylab("RMSE---MAE")+
labs(subtitle = "Modelo WRF a diferencia nivel")+
theme_light()
###############################DN-----AP
modelos_WRF_DN<- here::here('Data/Parques/Belesar/Modelos/DN_AP/') %>% list.files(full.names = T) %>%
.[str_detect(., ".RDS")]
Lista_results<- list()
for (i in 1:length(modelos_WRF_DN)) {
Lista_results[[i]]<- tryCatch({readRDS(modelos_WRF_DN[i])}, error=function(e){0})
}
Lista_modelos_buenos<- Lista_results[sapply(Lista_results, function(x) length(class(x))==2)]
names(Lista_modelos_buenos)<- Lista_modelos_buenos %>% sapply(., function(x) x$method)
################PARA LA PREDICCION DE DN A AP
prediccion_data_DN_A<- Tabla_2[Tabla_2$Date> ymd("2019/01/25"), ]
MODEL_DN_AP_PREDICTION<- predict(modelo_DN_AP, newdata= prediccion_data_DN_A)
OBSERVED_AP<- prediccion_data_DN_A$aport_SMA
modelos_WRF_DN<- here::here('Data/Parques/Belesar/Modelos/DN_AP/') %>% list.files(full.names = T) %>%
.[str_detect(., ".RDS")]
Lista_results<- list()
for (i in 1:length(modelos_WRF_DN)) {
Lista_results[[i]]<- tryCatch({readRDS(modelos_WRF_DN[i])}, error=function(e){0})
}
Lista_modelos_buenos<- Lista_results[sapply(Lista_results, function(x) length(class(x))==2)]
names(Lista_modelos_buenos)<- Lista_modelos_buenos %>% sapply(., function(x) x$method)
Lista_parameters<-list()
for (i in 1:length(Lista_modelos_buenos)) {
MODEL_WRF_DN_PREDICTION<- predict(Lista_modelos_buenos[[i]], newdata= prediccion_data_DN_A)
error<- OBSERVED_AP- MODEL_WRF_DN_PREDICTION
Lista_parameters[[i]] <- c(rmse(error), mae(error))
}
Tabla_results<- Lista_parameters %>% unlist() %>% matrix(ncol=2, byrow = T) %>% as.data.frame() %>%
cbind(names(Lista_modelos_buenos), . )
colnames(Tabla_results)<- c("Tipo_modelo", "RMSE", "MAE")
Tabla_results %>% group_by(Tipo_modelo) %>% summarise(MRMSE=min(RMSE),MMAE=min(MAE)) %>%
as.data.frame() %>%
ggplot()+
geom_bar(aes(y=MRMSE, x=Tipo_modelo),
stat = "identity",
fill="red",
alpha=0.5)+
geom_bar(aes(y=MMAE, x=Tipo_modelo),
stat = "identity",
fill="blue",
alpha=0.5)+
ylab("RMSE---MAE")+
labs(subtitle = "Modelo WRF a diferencia nivel")+
theme_light()